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Research Postulate #5: From this preliminary analysis, we postulate that it might be possible going forward, to forecast and anticipate any number of behavioral factors beneficial to marketers. These might be provided by big data through various consumer tracking studies and the like. In short, this might provide additional insights and value to traditional product and consumer life cycle analysis.
Conclusions and Next Steps
This paper is clearly exploratory and directional. It was developed primarily to create a scholarly discussion on the value and use of generational or age cohort data and variables in terms of generating a better understanding of how and in what way this type of data can be used
by researchers and marketers to improve marketing results. Much of this paper is devoted to suggesting
that generational analysis can be best understood using longitudinal or data gathered over time, i.e., big data. In too many instances today, we have seen researchers and marketers alike make pronouncements on marketplace situations using only one-time or snapshot results of limited groups of consumers. In this paper, we have argued that only by understanding the preceding or following age group can any sense be made of what is happing in the moment. Context, we argue is critical in understanding the various generations as they are the outcomes of a wide variety of factors, many of which cannot be understood unless evaluated in the context in which they are occurring. We strongly believe, and feel we have demonstrated in this paper, that generational analysis, or age cohort development is a moving element in the marketing landscape. It cannot be captured or understood unless viewed over time, something which too few researchers and marketers do today.
In conducting our analysis, we developed six postulates. These came directly from the data and analysis
which we conducted. We developed these factors as postulates rather than hypotheses. The data on which these were taken came directly from consumers. Thus, we did not inject any researcher bias or speculation. The data is what it is, which we feel, is the only true way for researchers to present their ndings in the age of big data. That said, our postulates are:
• Research Postulate #1: some habits and preferences generated in various generations continue throughout the life of the person while others change and evolve.
• Research Postulate #2: There are a number of psychological measures which define and explain how and why consumers behave as they do during certain age or generational periods. Thus, it is important to understand the underlying context in
which generational or age cohort research is or has been conducted.
• Research Postulate #3: Getting and giving advice has changed dramatically with the introduction of social media. It may well be that what is considered traditional “word-of-mouth” is being replaced by social media.
• Research Postulate #4: The sheer availability and prevalence of online and social media is not the primary reason for the growth of online shopping. Indeed, the younger groups of people do not appear to be impacted substantially more than other age groups or cohorts. Thus, social media, whether that be mobile or from a fixed location does not appear to be driving online shopping in the United States.
• Research Postulate #5: From this preliminary analysis, we postulate that it might be possible going forward, to forecast and anticipate any number of behavioral factors beneficial to marketers. These might be provided by big data through various consumer tracking studies and the like. In short, this might provide additional insights and value to traditional product and consumer life cycle analysis.
As noted earlier, while there is no substantial proof that these postulates are true or accurate, we believe they are substantial enough to enter the literature stream
to be tested and re ned or rejected as determined by future scholars. Thus, they are presented in this manner in this paper.
Limitations
There are clearly limitations on the ndings, recommendations and even the postulates presented in this paper. That comes primarily from the data and the analysis used in this paper. Those are brie y listed below.
1. All data was gathered only from online respondents in the United States. Other countries and other cultures may demonstrate significantly different responses and non-online consumers may be different as well.
2. While an argument is made in this paper for the use of longitudinal big data, many of our results have necessarily been truncated simply to fit
the requirements of the conference organizers. While we do not feel our results would change substantially with the inclusion of longer analytical periods or more depth of analysis, that is, of course possible.
3. This paper is clearly directional and exploratory. We have included a number of non-traditional
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